Earticle

초록

영어

There are various types of noises that affect quality of an image such as Salt-and-pepper noise, Poison noise, Gaussian noise, Speckle noise etc. Wavelet is a powerful tool for denoising a variety of signals. Here a White Flower image has been taken for denoising purpose with the help of HAAR Transform. The noisy image is first decomposed into five levels to obtain different frequency bands. Then hard thresholding method is used to remove the noisy coefficients by fixing the optimum thresholding value. In this paper, analysis of a colored image is carried out with four different noises at zero mean that are applied on the image to produce noisy images. Residual image is obtained from the original and noisy image & its statistical parameters such as mean, median, mode, standard deviation, mean absolute deviation, median absolute deviation are calculated. In order to enhance the quality of the noisy images, performance parameters of denoised images must be estimated. The comparison between noisy and denoised image is taken in terms of MSE (mean square error), PSNR (peak signal to noise ratio), RMSE (root mean square error), SNR (signal to noise ratio) and SSIM (structural similarity index).

목차

Abstract
 1. Introduction
 2. Discrete Wavelet Transform
 3. Wavelet Thresholding
 4. Performance Parameters
 5. Statistical Parameters
 6. Methodology
 7. Result
 8. Conclusion
 9. Future Work
 References

키워드

salt-and-pepper noise poison noise gaussian noise speckle noise Wavelet transform hard thresholding psnr snr rmse mse ssim

저자

• Prateek Kumar [ Department of Electronics Communication Engineering RustamJi Institute of Technology, BSF Academy, Tekanpur, Gwalior (M.P.)-INDIA ]
• Sandeep Kumar Agarwal [ Department of Electronics Communication Engineering RustamJi Institute of Technology, BSF Academy, Tekanpur, Gwalior (M.P.)-INDIA ]

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